Scholarlink

The conjugate gradient (CG) algorithm is a widely used approach for training neural networks. Its most computationally demanding step is directional minimization. This paper introduces a novel modification of the CG algorithm that accelerates directional minimization, leading to a significant reduction in computation time. The proposed modification was evaluated on selected test cases, and its performance was compared with the classical CG method.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-point Directional Minimization for Conjugate Gradient Algorithm

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Jarosław Bilski,
  • Jacek Smoląg

摘要

The conjugate gradient (CG) algorithm is a widely used approach for training neural networks. Its most computationally demanding step is directional minimization. This paper introduces a novel modification of the CG algorithm that accelerates directional minimization, leading to a significant reduction in computation time. The proposed modification was evaluated on selected test cases, and its performance was compared with the classical CG method.